‌Journal Information

IECE Transactions on Swarm and Evolutionary Learning

Online ISSN: request pending

Print    ISSN: request pending

Publishing model:  Hybrid

DOI Prefix: 10.62762/TSEL

Aims & Scope

IECE Transactions on Swarm and Evolutionary Learning is an international, peer-reviewed journal dedicated to advancing the theory, algorithms, and applications of swarm intelligence and evolutionary learning. The journal provides a platform for researchers, practitioners, and academicians to share groundbreaking developments and foster collaboration in this rapidly evolving interdisciplinary field.

Aims

The primary aim of this journal is to publish high-quality, original research articles, reviews, and case studies that contribute to the understanding and innovation in swarm intelligence, evolutionary computation, and their integration with other advanced learning paradigms. We focus on both fundamental theoretical studies and real-world applications that address complex optimization, decision-making, and learning problems.

Scope

The scope of IECE Transactions on Swarm and Evolutionary Learning includes, but is not limited to, the following areas:

1. Swarm Intelligence

• Particle swarm optimization (PSO), ant colony optimization (ACO), bee algorithms, and other bio-inspired swarm models

• Applications of swarm intelligence in robotics, networks, and real-world optimization problems

• Hybridization of swarm algorithms with machine learning and deep learning techniques

2. Evolutionary Learning and Computation

• Genetic algorithms, genetic programming, evolutionary strategies, and memetic algorithms

• Advanced evolutionary learning frameworks, including co-evolutionary systems and multi-objective optimization

• Theoretical analysis, convergence properties, and parameter tuning in evolutionary systems

3. Hybrid and Emerging Techniques

• Integration of swarm intelligence with deep learning, reinforcement learning, and neural networks

• Hybrid meta-heuristics and optimization frameworks for large-scale problems

• Nature-inspired systems and algorithms for big data analytics and AI-driven applications

4. Applications and Case Studies

• Real-world applications in engineering design, smart cities, healthcare systems, transportation, and logistics

• Applications of swarm and evolutionary learning in energy systems, IoT, robotics, and industrial automation

• Benchmark studies, software, and hardware implementations for performance evaluation

5. Future Directions and Emerging Challenges

• Explainable and interpretable swarm intelligence and evolutionary algorithms

• Quantum-inspired optimization and learning paradigms

• Ethical considerations and environmental impact of bio-inspired computation

IECE Transactions on Swarm and Evolutionary Learning seeks to bridge the gap between theoretical advancements and practical implementations, promoting the dissemination of innovative solutions to global challenges. We encourage interdisciplinary contributions that connect swarm and evolutionary learning with emerging technologies to address the most pressing computational and optimization problems of today.

Publication Frequency

Quarterly

Ownership

The journal is owned by Institute of Emerging and Computer Engineering.

Archiving

All journals published by IECE are archived in Portico, which provides permanent digital archiving for scholarly journals.

Ethics Statement

IECE is responsible for implementing rigorous peer review and strict ethical policies and standards to ensure that high quality scientific work is added to the field of scholarly publishing. IECE takes such publishing ethics issues very seriously, and our editors are trained to enforce COPE's Core Practices and Guidelines, with a zero-tolerance policy for plagiarism, data falsification, and other behaviours. To verify the originality of content submitted to our journals, we use iThenticate to check submissions against previous publications.